The present invention generally relates to an expression cassette comprising a 3′-UTR cDNA library fragment, mammalian cells transfected with the expression cassette, and kits comprising the same. Furthermore, methods for identifying target genes for microRNAs are provided that utilize the expression cassette hereof.
MicroRNAs are a class of naturally-occurring small non-coding RNAs that control gene expression by translational repression or mRNA degradation (1-3). They are abundantly expressed and could comprise 1-5% of animal genes (4). Since the discovery of lin-4 and let-7 in Caenorhabditis elegans (5-7), over six thousand microRNAs have been identified in a variety of organisms, including plants, flies and animals through the genomics and bioinformatics effort (8). Like protein-coding genes, microRNAs are transcribed as long primary transcripts (pri-microRNAs) in the nucleus. However, distinct from protein-coding genes, they are subsequently cleaved to produce stem loop structured precursor molecules (pre-microRNAs) of 70-100 nucleotides (nt) in length by the nuclear RNase III enzyme Drosha (9). The pre-microRNAs are then exported to the cytoplasm by exportin-5 (10) where the RNase III enzyme Dicer further processes them into mature microRNAs (˜22 nt). One strand of the microRNA duplex is subsequently incorporated into the RNA-induced silencing complex (RISC) that mediates target gene expression. Although the microRNA pathways leading to gene silencing are not fully understood yet, evidence indicates that they target mRNAs for translational repression or mRNA cleavage (11, 12). Since microRNAs are able to silence gene expression by binding to the 3′-untranslated region (3′-UTR) of the gene with partial sequence homology, a single microRNA usually has multiple targets (13). Thus, microRNAs could regulate a large fraction of protein-coding genes. Indeed, as high as 30% of all genes could be microRNA targets (11, 14). In essence, microRNAs can be considered to be modulators of gene regulators and they can cooperate with transcription factors. Together, microRNAs and transcription factors determine gene expression patterns in the cell (15). Therefore, the discovery of microRNAs adds a new layer of gene regulation to the complex gene expression network.
Given the important role of microRNA in regulating cellular pathways, it has been found that a unique set of microRNAs (or microRNA signatures) are often associated with human cancer. Lu et al. reported a general downregulation of a number of microRNAs in tumors compared with normal tissues in multiple human cancers (16). Of considerable interest, microRNA expression profiles are able to successfully classify poorly differentiated tumors whereas mRNA profiles are highly inaccurate for the same samples (16). MicroRNA signatures have also been reported in other types of cancers, including chronic lymphocytic leukemia (CLL) (17), lung cancer (18), pituitary adenomas (19), uterine leiomyomas (20) and adult acute myeloid leukemia (AML) (21). In lung cancer, microRNA expression profiles correlate with survival of lung adenocarcinomas, including those classified as disease stage I; high miR-155 and low let-7a-2 expression correlates with poor survival (18). Hierarchical clustering analysis of microRNA expression profiles is able to distinguish tumor from normal pancreas, pancreatitis and cell lines (22). In pituitary adenomas, 30 microRNAs are differentially expressed between normal pituitary and pituitary adenomas and among them, 24 microRNAs can serve as a predictive signature of pituitary adenoma and 29 microRNAs are able to predict pituitary adenoma histotype (19). In human uterine leiomyomas, 31 of 206 microRNAs examined reveal a distinct microRNA expression profile associated with tumor size and race (20). More interestingly, a solid cancer microRNA signature is suggested by a large portion of overexpressed microRNAs from a large-scale miRnome analysis on 540 samples, including lung, breast, stomach, prostate, colon, and pancreatic tumors (23). Together, these findings highlight the potential of microRNA profiling in cancer diagnosis (16).
The fundamental role of microRNAs in regulating cellular pathways suggests that deregulation of microRNAs affects normal cell growth and development, leading to a variety of disorders including neurological diseases (24) and human cancer (12, 25-28). Specific overexpression or underexpression has been shown to correlate with particular tumor types (16, 17, 32-34) because overexpression of a particular set of microRNAs could result in down-regulation of tumor suppressor genes, whereas their underexpression could lead to oncogene up-regulation, suggesting that microRNAs may function as either oncogenes or tumor suppressor genes (29). Since microRNAs are often located at fragile sites or in repetitive genomic sequences of chromosomal regions (30), this may explain why microRNA expression deregulation occurs frequently in human cancer. For instance, 68% of investigated patients suffering from B-cell chronic lymphocytic leukemia (CLL) have been shown to have a deletion located at chromosome 13q14 where the miR-15 and miR-16 genes reside and are under-represented in many B-CLL patients (31).
Apparently, whether a microRNA functions as an oncogene or tumor suppressor is largely determined by the target genes of each particular microRNA. For example, tumor suppressive microRNAs, such as let-7, miR-15 and miR-16, are able to suppress expression of oncogenes. let-7 suppresses ras oncogene and is downregulated in lung cancer (32); miR-15 and miR-16 suppress Bcl-2 anti-apoptotic gene, and they are deleted or downregulated in leukemia (31, 33). In contrast, oncogenic microRNAs can silence tumor suppressor genes. miR-17-5p and miR-20a control the balance of cell death and proliferation driven by the proto-oncogene c-Myc (34) and miR-17-5p serves as an oncogene in lymphoma and lung cancer (35, 36). Similarly, a cluster consisting of miR-372 and miR-373 have been shown to function as oncogenes in testicular germ cell tumors by suppressing the p53 pathway (37). Moreover, it has been demonstrated by the present inventors and others that antisense miR-21 oligonucleotide suppresses tumor cell growth which is associated with increased apoptosis and decreased cell proliferation (38, 39) thereby suggesting that miR-21 is an oncogene. The present inventors and others subsequently identified the tumor suppressor gene tropomyosin 1 (TPM1) as a direct miR-21 target gene (40). Furthermore, miR-21 also plays a role in cell invasion and tumor metastasis, which is likely through regulation of multiple miR-21 target genes, such as TPM1, programmed cell death 4 (pdcd4) and maspin (41). Of interest, certain microRNAs may specifically modulate only tumor metastasis. For example, miR-10b functions as a metastasis initiation factor and overexpression of miR-10b causes breast tumor invasion and metastasis, but it has no effect on primary tumor growth (42). On the other hand, miR-335 suppresses metastasis and migration through targeting of the progenitor cell transcription factor SOX4 and extracellular matrix component tenascin C (43).
Since microRNAs regulate cellular pathways by suppression of their specific target genes, identification of microRNA target genes is critical to the understanding of molecular mechanisms of microRNA-mediated tumorigenesis. Computational algorithms have been a major driving force in predicting microRNA targets (44-46). The approaches are mainly based on base pairing between microRNA and target gene 3′-UTR, emphasizing the location of microRNA complementary elements in 3′-UTR of target mRNAs, the concentration in the seed sequence (6-8 bp) of continuous Watson-Crick base pairing in 5′ proximal half of the microRNA and the phylogenetic conservation of the complementary sequences in 3′-UTRs of orthologous genes. They provide very useful primary sources in search for microRNA targets. However, despite the fundamentally similar approaches used for the published screens for microRNA targets, predicted targets for a given microRNA often vary among different methods. Presumably the approaches differ in certain important details, such as defining phylogenetic conservation, thermodynamic and statistical factors applied to score and rank predicted sites. The fact that mature microRNAs are short and typically contain several sequence mismatches with their target transcripts has complicated computational target predictions. This might explain why computer-aided algorithms are still unable to provide a precise picture of microRNA regulatory networks. In addition, a recent report indicates that perfect seed pairing is not a generally reliable predictor for miRNA-target interactions at least in some cases (47, 48), which further highlights the difficulty of microRNA target predictions. Thus, they can only serve a complementary approach and certainly need in vivo experimental validations. Another challenge is that it is not clear whether a microRNA can target mRNA which does not carry a putative binding site for this microRNA. If this is the case, such a target gene may escape from these prediction methods because all of them are mainly based on sequence homology between microRNA and mRNA. More recently, there are reports that microRNAs are able to bind to 5′-UTR or coding regions and silence or even enhance the corresponding genes. These findings suggest that microRNAs are not necessarily restricted to the 3′-UTR to exert their function. However, the currently prediction methods are mainly based on the 3′-UTR. In other words, some microRNA targets would also escape from these prediction methods.
Regarding microRNA prediction methods, currently there is no clear consensus as to which one is most reliable. The present inventors have compared four commonly cited microRNA target prediction programs, TargetScan4 (49), miRBase Target5 (http://microrna.sanger.ac.uk/targets/v5/), PicTar (50) and miRanda (http://www.microma.org) (51). In general, miRBase Target5 and miRanda tend to predict more targets than TargetScan4 or PicTar does presumably because the first two programs do not weigh as much on conservations among different species as the other two programs. Using miR-21 as an example, miRBase Target5 and miRanda predict 1000 and 2501 targets, respectively. On the other hand, TargetScan4 and PicTar predict 186 and 175 targets, respectively. However, only a small fraction of predicted targets among these methods overlap thereby suggesting that each method has its own unique set of parameters. For example, some of these models have recently been refined to consider the presence of secondary structures and other features of the 3′-UTR sequence surrounding the target site, and for the ability of complementarity at the 3′ end of the cognate miRNA to compensate for imperfect seed matching (49, 52). Nevertheless, despite these efforts, little is known about the prediction accuracy of these methods because only a very limited number of targets have been experimentally validated. Therefore, there is a need in the art for systematic target validation methods.
Microarray technology could be one of target validation approaches because it is capable of determining expression of potential microRNA targets at the mRNA level (53, 54). However, given that a large fraction of microRNA target genes are silenced by the translation repression mechanism, those microRNA targets may escape from the microarray detection. Alternatively, microRNAs can be used as endogenous cytoplasmic primers to synthesize cDNA on an mRNA template (55) such that recovered primers would presumably be functional microRNAs. However, this is technically challenging because of limited sequence homology between mRNA and microRNA. In addition, it could be extremely difficulty to recover those microRNAs that can cause mRNA degradation. Alternatively, biochemical or proteomic methods have been used for this purpose (56-61), but they could be labor intensive.
Currently, in research laboratories a common approach to validate whether a gene is a direct microRNA target involves cloning of the 3′-UTR of this gene into a reporter (e.g., luciferase), followed by reporter assays. It is further verified to be suppressed by a given microRNA at the mRNA level (e.g., real-time RT-PCR) or at the protein level (e.g., Western blot). Apparently, validation of multiple microRNA targets with this approach needs a high throughput screening system because each microRNA will have to be individually tested against a given UTR sequence, which requires intensive labor and costly reagents (FIG. 13 right). Therefore, the selection system described here will save tremendous time and cost because this method allows selection of positive microRNA/mRNA interactions (FIG. 13 left).
The genetic selection method of the present invention represents a unique systematic validation system for microRNA targets that provides a comprehensive picture of microRNA/mRNA interactions for a given gene or a given microRNA. One of the advantages of this system is that it allows for the determination of microRNA/mRNA interactions whether mRNA degradation or translation repression is involved or whether conserved microRNA binding sites are required. Moreover, this is a simple but powerful selection method that does not require intensive labor or costly instrument and reagents and it is suitable for a large number of microRNAs or target genes.
The following references that are referred throughout this disclosure are hereby incorporated by reference in their entirety to the extent permitted by law. These references merely serve to support the invention and to provide background and context. Applicant reserves the right to challenge the veracity of any statements therein made.
1. Pillai RS MicroRNA function: multiple mechanisms for a tiny RNA? Rna 2005; 11:1753-1761.
2. Zamore P D, Haley B Ribo-gnome: the big world of small RNAs. Science 2005; 309:1519-1524.
3. Bartel D P MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116:281-297.
4. Berezikov E, Guryev V, van de Belt J, Wienholds E, Plasterk R H, Cuppen E Phylogenetic shadowing and computational identification of human microRNA genes. Cell 2005; 120:21-24.
5. Lee R C, Feinbaum R L, Ambros V The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993; 75:843-854.
6. Wightman B, Ha I, Ruvkun G Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 1993; 75:855-862.
7. Reinhart B J. Slack F J. Basson M. Pasquinelli A E. Bettinger J C. Rougvie A E, et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000; 403:901-906.
8. Griffiths-Jones S, Saini H K, van Dongen S, Enright A J miRBase: tools for microRNA genomics. Nucleic Acids Res 2008; 36:D154-158.
9. Kim V N MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol 2005; 6:376-385.
10. Yi R, Qin Y, Macara I G, Cullen B R Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 2003; 17:3011-3016.
11. Du T, Zamore PD microPrimer: the biogenesis and function of microRNA. Development 2005; 132:4645-4652.
12. Esquela-Kerscher A, Slack F J OncomiRs—microRNAs with a role in cancer. Nat Rev Cancer 2006; 6:259-269.
13. Brennecke J, Stark A, Russell R B, Cohen S M Principles of microRNA-target recognition. PLoS Biol 2005; 3:e85.
14. Lewis B P, Burge C B, Bartel D P Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005; 120:15-20.
15. Hobert O Gene regulation by transcription factors and microRNAs. Science 2008; 319:1785-1786.
16. Lu J. Getz G. Miska E A. Alvarez-Saavedra E. Lamb J. Peck D, et al. MicroRNA expression profiles classify human cancers. Nature 2005; 435:834-838.
17. Calin G A. Ferracin M. Cimmino A. Di Leva G. Shimizu M. Wojcik S E, et al. A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 2005; 353:1793-1801.
18. Yanaihara N. Caplen N. Bowman E. Seike M. Kumamoto K. Yi M, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006; 9:189-198.
19. Bottoni A. Zatelli M C. Ferracin M. Tagliati F. Piccin D. Vignali C, et al. Identification of differentially expressed microRNAs by microarray: a possible role for microRNA genes in pituitary adenomas. J Cell Physiol 2007; 210:370-377.
20. Wang T. Zhang X. Obijuru L. Laser J. Aris V. Lee P, et al. A micro-RNA signature associated with race, tumor size, and target gene activity in human uterine leiomyomas. Genes Chromosomes Cancer 2007; 46:336-347.
21. Debernardi S, Skoulakis S, Molloy G, Chaplin T, Dixon-McIver A, Young B D MicroRNA miR-181a correlates with morphological sub-class of acute myeloid leukaemia and the expression of its target genes in global genome-wide analysis. Leukemia 2007.
22. Lee E J. Gusev Y. Jiang J. Nuovo G J. Lerner M R. Frankel W L, et al. Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer 2007; 120:1046-1054.
23. Volinia S. Calin G A. Liu C G. Ambs S. Cimmino A. Petrocca F, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 2006; 103:2257-2261.
24. Dostie J, Mourelatos Z, Yang M, Sharma A, Dreyfuss G Numerous microRNPs in neuronal cells containing novel microRNAs. Rna 2003; 9:180-186.
25. Hwang H W, Mendell J T MicroRNAs in cell proliferation, cell death, and tumorigenesis. Br J Cancer 2006; 94:776-780.
26. Croce C M, Calin G A miRNAs, cancer, and stem cell division. Cell 2005; 122:6-7.
27. Hammond S M MicroRNAs as oncogenes. Curr Opin Genet Dev 2006; 16:4-9.
28. Gregory R I, Shiekhattar R MicroRNA biogenesis and cancer. Cancer Res 2005; 65:3509-3512.
29. Chen C Z MicroRNAs as oncogenes and tumor suppressors. N Engl J Med 2005; 353:1768-1771.
30. Calin G A. Liu C G. Sevignani C. Ferracin M. Felli N. Dumitru C D, et al. MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias. Proc Natl Acad Sci USA 2004; 101:11755-11760.
31. Calin G A. Dumitru C D. Shimizu M. Bichi R. Zupo S. Noch E, et al. Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA 2002; 99:15524-15529.
32. Johnson S M. Grosshans H. Shingara J. Byrom M. Jarvis R. Cheng A, et al. RAS is regulated by the let-7 microRNA family. Cell 2005; 120:635-647.
33. Cimmino A. Calin G A. Fabbri M. Iorio M V. Ferracin M. Shimizu M, et al. miR-15 and miR-16 induce apoptosis by targeting BCL2. Proc Natl Acad Sci USA 2005; 102:13944-13949.
34. O'Donnell K A, Wentzel E A, Zeller K I, Dang C V, Mendell J T c-Myc-regulated microRNAs modulate E2F1 expression. Nature 2005; 435:839-843.
35. He L. Thomson J M. Hemann M T. Hernando-Monge E. Mu D. Goodson S, et al. A microRNA polycistron as a potential human oncogene. Nature 2005; 435:828-833.
36. Hayashita Y. Osada H. Tatematsu Y. Yamada H. Yanagisawa K. Tomida S, et al. A polycistronic microRNA cluster, miR-17-92, is overexpressed in human lung cancers and enhances cell proliferation. Cancer Res 2005; 65:9628-9632.
37. Voorhoeve P M. le Sage C. Schrier M. Gillis A J. Stoop H. Nagel R, et al. A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ cell tumors. Cell 2006; 124:1169-1181.
38. Si M L, Zhu S, Wu H, Lu Z, Wu F, Mo Y Y miR-21-mediated tumor growth. Oncogene 2007; 26:2799-2803.
39. Chan J A, Krichevsky A M, Kosik K S MicroRNA-21 is an antiapoptotic factor in human glioblastoma cells. Cancer Res 2005; 65:6029-6033.
40. Zhu S, Si M L, Wu H, Mo Y Y MicroRNA-21 targets the tumor suppressor gene tropomyosin 1 (TPM1). J Biol Chem 2007.
41. Zhu S, Wu H, Wu F, Nie D, Sheng S, Mo Y Y MicroRNA-21 targets tumor suppressor genes in invasion and metastasis. Cell Res 2008; 18:350-359.
42. Ma L, Teruya-Feldstein J, Weinberg R A Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 2007; 449:682-688.
43. Tavazoie S F. Alarcon C. Oskarsson T. Padua D. Wang Q. Bos PD, et al. Endogenous human microRNAs that suppress breast cancer metastasis. Nature 2008; 451:147-152.
44. Stark A, Brennecke J, Russell R B, Cohen S M Identification of Drosophila MicroRNA targets. PLoS Biol 2003; 1:E60.
45. Lewis B P, Shih I H, Jones-Rhoades M W, Bartel D P, Burge C B Prediction of mammalian microRNA targets. Cell 2003; 115:787-798.
46. Kiriakidou M, Nelson P T, Kouranov A, Fitziev P, Bouyioukos C, Mourelatos Z, et al. A combined computational-experimental approach predicts human microRNA targets. Genes Dev 2004; 18:1165-1178.
47. Didiano D, Hobert 0 Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat Struct Mol Biol 2006; 13:849-851.
48. Didiano D, Hobert O Molecular architecture of a miRNA-regulated 3′ UTR. Rna 2008.
49. Grimson A, Farh K K, Johnston W K, Garrett-Engele P, Lim L P, Bartel D P MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 2007; 27:91-105.
50. Krek A. Grun D. Poy M N. Wolf R. Rosenberg L. Epstein E J, et al. Combinatorial microRNA target predictions. Nat Genet 2005; 37:495-500.
51. John B, Enright A J, Aravin A, Tuschl T, Sander C, Marks D S Human MicroRNA targets. PLoS Biol 2004; 2:e363.
52. Long D, Lee R, Williams P, Chan C Y, Ambros V, Ding Y Potent effect of target structure on microRNA function. Nat Struct Mol Biol 2007; 14:287-294.
53. Huang J C. Babak T. Corson T W. Chua G. Khan S. Gallie B L, et al. Using expression profiling data to identify human microRNA targets. Nat Methods 2007; 4:1045-1049.
54. Lim L P. Lau N C. Garrett-Engele P. Grimson A. Schelter J M. Castle J, et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 2005; 433:769-773.
55. Vatolin S, Navaratne K, Weil R J A novel method to detect functional microRNA targets. J Mol Biol 2006; 358:983-996.
56. Karginov F V, Conaco C, Xuan Z, Schmidt B H, Parker J S, Mandel G, et al. A biochemical approach to identifying microRNA targets. Proc Natl Acad Sci USA 2007; 104:19291-19296.
57. Zhu S, Si M L, Wu H, Mo Y Y MicroRNA-21 Targets the Tumor Suppressor Gene Tropomyosin 1 (TPM1). J Biol Chem 2007; 282:14328-14336.
58. Easow G, Teleman A A, Cohen S M Isolation of microRNA targets by miRNP immunopurification. Rna 2007; 13:1198-1204.
59. Beitzinger M, Peters L, Zhu J Y, Kremmer E, Meister G Identification of human microRNA targets from isolated argonaute protein complexes. RNA Biol 2007; 4:76-84.
60. Zhang L. Ding L. Cheung T H. Dong M Q. Chen J. Sewell A K, et al. Systematic identification of C. elegans miRISC proteins, miRNAs, and mRNA targets by their interactions with GW182 proteins AIN-1 and AIN-2. Mol Cell 2007; 28:598-613.
61. Hendrickson D G, Hogan D J, Herschlag D, Ferrell J E, Brown P O Systematic identification of mRNAs recruited to argonaute 2 by specific microRNAs and corresponding changes in transcript abundance. PLoS ONE 2008; 3:e2126.
62. Mo Y Y, Wang C, Beck W T A novel nuclear localization signal in human DNA topoisomerase I. J Biol Chem 2000; 275:41107-41113.
63. Wu F, Chiocca S, Beck W T, Mo Y Y Gam 1-associated alterations of drug responsiveness through activation of apoptosis. Mol Cancer Ther 2007; 6:1823-1830.
64. Margolin J F, Friedman J R, Meyer W K, Vissing H, Thiesen H J, Rauscher F J, 3rd Kruppel-associated boxes are potent transcriptional repression domains. Proc Natl Acad Sci USA 1994; 91:4509-4513.
65. Deuschle U, Meyer W K, Thiesen H J Tetracycline-reversible silencing of eukaryotic promoters. Mol Cell Biol 1995; 15:1907-1914.
66. Herchenroder O, Hahne J C, Meyer W I, Thiesen H J, Schneider J Repression of the human immunodeficiency virus type 1 promoter by the human KRAB domain results in inhibition of virus production. Biochim Biophys Acta 1999; 1445:216-223.
67. Rittner K, Schultz H, Pavirani A, Mehtali M Conditional repression of the E2 transcription unit in E1-E3-deleted adenovirus vectors is correlated with a strong reduction in viral DNA replication and late gene expression in vitro. J Virol 1997; 71:3307-3311.
68. Cmarik J L. Min H. Hegamyer G. Zhan S. Kulesz-Martin M. Yoshinaga H, et al. Differentially expressed protein Pdcd4 inhibits tumor promoter-induced neoplastic transformation. Proc Natl Acad Sci USA 1999; 96:14037-14042.
69. Lau A T, Chiu J F The possible role of cytokeratin 8 in cadmium-induced adaptation and carcinogenesis. Cancer Res 2007; 67:2107-2113.
70. Zhu S W H, Wu F, Nie D, Sheng S, Mo Y Y. MicroRNA-21 targets tumor suppressor genes in invasion and metastasis. Cell Research 2007; In press.
71. Stark A, Brennecke J, Bushati N, Russell R B, Cohen S M Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3′UTR evolution. Cell 2005; 123:1133-1146.
72. Ørom U A, Nielsen F C, Lund A H, MicroRNA-10a binds the 5′UTR of ribosomal protein mRNAs and enhances their translation, 1. Mol Cell. 2008 May 23; 30(4):460-71.
73. Forman J J, Legesse-Miller A, Coller H A. A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence. Proc Natl Acad Sci USA. 2008 Sep. 30; 105(39):14879-84.
74. Tay Y, Zhang J, Thomson A M, Lim B, Rigoutsos I. MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature. 2008 September 17.
75. Baek D, Villen J, Shin C, Camargo F D, Gygi S P, Bartel D P, The impact of microRNAs on protein output, Nature. 2008 Sep. 4; 455(7209):64-71 Epub 2008 July 30.
76. Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N., Widespread changes in protein synthesis induced by microRNAs, Nature. 2008 Sep. 4; 455(7209):58-63. Epub 2008 July 30.
77. Zhu S, Wu H, Wu F, Nie D, Sheng S, Mo Y Y. MicroRNA-21 targets tumor suppressor genes in invasion and metastasis. Cell Res. 2008 March; 18(3):350-9.